Abstract
BackgroundAccurate determination of mouse positions from video data is crucial for various types of behavioral analyses. While detection of body positions is straightforward, the correct identification of nose positions, usually more informative, is far more challenging. The difficulty is largely due to variability in mouse postures across frames.ResultsHere, we present OptiMouse, an extensively documented open-source MATLAB program providing comprehensive semiautomatic analysis of mouse position data. The emphasis in OptiMouse is placed on minimizing errors in position detection. This is achieved by allowing application of multiple detection algorithms to each video, including custom user-defined algorithms, by selection of the optimal algorithm for each frame, and by correction when needed using interpolation or manual specification of positions.ConclusionsAt a basic level, OptiMouse is a simple and comprehensive solution for analysis of position data. At an advanced level, it provides an open-source and expandable environment for a detailed analysis of mouse position data.
Highlights
Accurate determination of mouse positions from video data is crucial for various types of behavioral analyses
Results we briefly describe a few examples that provide some general insights on analysis of position data using OptiMouse
This analysis is based on a two-odor preference test in a rectangular arena, but these principles apply to a broad range of arena configurations
Summary
We briefly describe a few examples that provide some general insights on analysis of position data using OptiMouse. The key point which is immediately apparent from inspection of the position displays and zone preference scores in Fig. 18 is that the annotated and non-annotated data yield very similar results. This comparison indicates that, for these examples, correct detection is achieved in the majority of frames, and that the failures that do occur, do not have a prominent influence on the final results. The second row shows enrichment scores of body positions in each of five different zones, whose coordinates relative to the arena are shown at the bottom of the figure. These tags are useful for identifying sessions for subsequent population level analyses of position data
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